Dev.to AI 🤖 Ai 👁 0 📖 2 min read

How to Turn Any URL into Clean Markdown for AI Agents & Run Instant SEO Audits (WebPulse API)

If you are building AI agents, RAG (Retrieval-Augmented Generation) pipelines, or automated scraping workflows, you already know the painful truth: raw web HTML is an absolute token burner. Feeding a full raw webpage in

If you are building AI agents, RAG (Retrieval-Augmented Generation) pipelines, or automated scraping workflows, you already know the painful truth: raw web HTML is an absolute token burner.

Feeding a full raw webpage into GPT-4, Claude, or Gemini wastes up to 80% of your context window on navigation bars, cookie banners, tracking scripts, and inline SVGs.

To solve this, I built WebPulse AI & SEO Suite API: a lightweight, ultra-fast 5-in-1 web extraction API that converts noisy URLs into clean, token-optimized Markdown, extracts rich metadata, audits technical SEO, and discovers B2B contact info in milliseconds.

Here is how it works and how you can test it for free today.

⚡ 1. The Core Problem: LLM Token Bloat

When you fetch a typical blog post or documentation page:

  • Raw HTML size: ~250 KB (approx. 60,000 tokens)
  • Actual readable content: ~15 KB (approx. 3,500 tokens)

Sending raw HTML to your LLM costs real money, slows down responses, and degrades reasoning accuracy.

With the /api/v1/markdown endpoint, WebPulse strips away all boilerplate and returns:

  1. Pure GitHub-flavored Markdown.
  2. Formatted plain text.
  3. Pre-split semantic rag_chunks ready to embed into vector databases (Pinecone, Chroma, Supabase).
  4. An automated calculation of your exact token savings percentage (llm_token_savings_percent).

Quick Python Example:

import requests

url = "https://webpulse-ai-markdown-seo-audit-and-contacts.p.rapidapi.com/api/v1/markdown"
querystring = {"url": "https://stripe.com"}

headers = {
"x-rapidapi-key": "YOUR_RAPIDAPI_KEY",
"x-rapidapi-host": "webpulse-ai-markdown-seo-audit-and-contacts.p.rapidapi.com"
}

response = requests.get(url, headers=headers, params=querystring)
data = response.json()

print(f"Token savings: {data['data']['llm_token_savings_percent']}%")
print(f"Clean Markdown sample:\n{data['data']['markdown'][:300]}...")

🔍 2. Instant 25-Point Technical SEO Audit

Most SEO tools are heavy, expensive, or require full browser emulation.

The /api/v1/seo-audit endpoint runs a 25-checkpoint audit directly at the HTTP and DOM level in under 150ms:

  • SEO Score (0–100) & Grade (A+ to F)
  • Meta title, description, and canonical tag validation
  • Heading structure hierarchy (H1 to H6 check)
  • Missing image alt attributes count
  • Internal vs external link balance
  • Security & indexability verification (robots, HTTPS)
  • Structured lists of critical_issues, warnings, and passed_checks

Perfect for agencies offering automated client onboarding reports or developers building automated website monitors.

💼 3. B2B Contact & Social Profile Discovery

Need to enrich company profiles automatically?

The /api/v1/contacts endpoint crawls the target page and detects:

  • Corporate & support emails (categorized by role vs direct address)
  • Phone numbers
  • 10 major social networks: LinkedIn, X (Twitter), GitHub, YouTube, Instagram, Facebook, TikTok, Discord, Telegram, WhatsApp
  • Key corporate pages: Pricing, Contact Us, About, Privacy Policy

🚀 4. How to Test It in 60 Seconds

You can test WebPulse immediately in two ways:

  1. Interactive Web Playground (No login required):
    Try live URLs directly in your browser:
    👉 https://webpulseapi.vercel.app

  2. RapidAPI Marketplace (50 Free Requests / Month):
    Subscribe to the Free Tier ($0/month) to get your API key:
    👉 https://rapidapi.com/Juanamigazo/api/webpulse-ai-markdown-seo-audit-and-contacts

🛠️ Built for Developers, AI Builders & Automation Enthusiasts

Whether you are chaining nodes in n8n / Make / Zapier, fine-tuning local LLMs, or aggregating market intelligence, WebPulse is built to be fast, reliable, and cost-effective.

Feedback is deeply appreciated! What other endpoints or data extraction features would make your AI or scraping pipelines smoother? Drop a comment below!

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